where \(\mu \)  and \(\Sigma \)  denote the mean and covariance of the real (x) and generated (g) samples. In this work, we introduce a measure to evaluate the quality of samples from unpaired image-to-image translators such as cycleGANs. We utilize a similar methodology to Heusel et al. in that we utilize a pretrained deep neural network to embed the samples. We choose the Resnet-50 architecture [1]} pretrained on ImageNet, as it has been shown to enable similarity searching in the embedding space for remote sensing imagery even without fine-tuning [2]}. Since the quality of the samples in this problem setting is determined not only by their realism but also by their “closeness” to the target domain, we evaluate the average cosine distances between each generated sample and each real sample in the Resnet-50 embedding space. The cosine Resnet distance (CRD) is then defined:
\(CRD = \frac{1}{N} \sum \limits _{i=1}^N \frac{1}{M} \sum \limits _{j=1}^M G[i] \cdot X[j]\) 
